OpenRaiser/Intern-Atlas huggingface dataset:
Molecular-scale Integration of Multi-modal Sensing and Neuromorphic Computing with Organic Electrochemical Transistors
Venue: arXiv cs.ET 2022
Year: 2022

Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint. However, digital hardware implementation of bionic learning suffers from device heterogeneity in sensors and processing cores, which incurs large hardware, energy and time overheads. Here, we present a universal solution to simultaneously perform multi-modal sensing, memory and processing using organic electrochemical transistors with designed architecture and tailored channel morphology, selective ion injection into the crystalline/amorphous regions. The resultant device work as either a volatile receptor that shows multi-modal sensing, or a non-volatile synapse that features record-high 10-bit analog states, low switching stochasticity and good retention without the integration of any extra devices. Homogeneous integration of such devices enables bionic learning functions such as conditioned reflex and real-time cardiac disease diagnose via reservoir computing, illustrating the promise for future smart edge health informatics.
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
Venue: arXiv cs.AI 2022
Year: 2022

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial-temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a "matrix-format" city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to extract dependency among "semantic neighbors". The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that "thinking beyond spatial neighbors" can further improve short-term travel demand prediction of urban bike sharing systems.
Leveraging Experience in Lifelong Multi-Agent Pathfinding
Venue: arXiv cs.MA 2022
Year: 2022

In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackled by partitioning it into multiple consecutive, and hence similar, "one-shot" MAPF queries, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents' start and goal positions, and how collisions need to be resolved from one query to the next. Thus, experience from solving one MAPF query can potentially be used to speedup solving the next one. Despite this intuition, current L-MAPF planners solve consecutive MAPF queries from scratch. In this paper, we introduce a new RHCR-inspired approach called exRHCR, which exploits experience in its constituent MAPF queries. In particular, exRHCR employs an extension of Priority-Based Search (PBS), a state-of-the-art MAPF solver. The extension, which we call exPBS, allows to warm-start the search with the priorities between agents used by PBS in the previous MAPF instances. We demonstrate empirically that exRHCR solves L-MAPF instances up to 39% faster than RHCR, and has the potential to increase system throughput for given task streams by increasing the number of agents a planner can cope with for a given time budget.
Deep Feature Rotation for Multimodal Image Style Transfer
Venue: arXiv cs.CV 2022
Year: 2022

Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. Our code is available at https://github.com/sonnguyen129/deep-feature-rotation.
Assessing the alignment between the information needs of developers and the documentation of programming languages: A case study on Rust
Venue: arXiv cs.SE 2022
Year: 2022

Programming language documentation refers to the set of technical documents that provide application developers with a description of the high-level concepts of a language. Such documentation is essential to support application developers in the effective use of a programming language. One of the challenges faced by documenters (i.e., personnel that produce documentation) is to ensure that documentation has relevant information that aligns with the concrete needs of developers. In this paper, we present an automated approach to support documenters in evaluating the differences and similarities between the concrete information need of developers and the current state of documentation (a problem that we refer to as the topical alignment of a programming language documentation). Our approach leverages semi-supervised topic modelling to assess the similarities and differences between the topics of Q&A posts and the official documentation. To demonstrate the application of our approach, we perform a case study on the documentation of Rust. Our results show that there is a relatively high level of topical alignment in Rust documentation. Still, information about specific topics is scarce in both the Q&A websites and the documentation, particularly related topics with programming niches such as network, game, and database development. For other topics (e.g., related topics with language features such as structs, patterns and matchings, and foreign function interface), information is only available on Q&A websites while lacking in the official documentation. Finally, we discuss implications for programming language documenters, particularly how to leverage our approach to prioritize topics that should be added to the documentation.
Co-WIN: Really Winning? Analysing Inequity in India's Vaccination Response
Venue: arXiv cs.CY 2022
Year: 2022

The COVID-19 pandemic has so far accounted for reported 5.5M deaths worldwide, with 8.7% of these coming from India. The pandemic exacerbated the weakness of the Indian healthcare system. As of January 20, 2022, India is the second worst affected country with 38.2M reported cases and 487K deaths. According to epidemiologists, vaccines are an essential tool to prevent the spread of the pandemic. India's vaccination drive began on January 16, 2021 with governmental policies being introduced to prioritize different populations of the society. Through the course of the vaccination drive, multiple new policies were also introduced to ensure that vaccines are readily available and vaccination coverage is increased. However, at the same time, some of the government policies introduced led to unintended inequities in the populations being targeted. In this report, we enumerate and analyze the inequities that existed in India's vaccination policy drive, and also compute the effect of the new policies that were introduced. We analyze these potential inequities not only qualitatively but also quantitatively by leveraging the data that was made available through the government portals. Specifically, (a) we discover inequities that might exist in the policies, (b) we quantify the effect of new policies introduced to increase vaccination coverage, and (c) we also point the data discrepancies that exist across different data sources.
Object-Guided Day-Night Visual Localization in Urban Scenes
Venue: arXiv cs.CV 2022
Year: 2022

We introduce Object-Guided Localization (OGuL) based on a novel method of local-feature matching. Direct matching of local features is sensitive to significant changes in illumination. In contrast, object detection often survives severe changes in lighting conditions. The proposed method first detects semantic objects and establishes correspondences of those objects between images. Object correspondences provide local coarse alignment of the images in the form of a planar homography. These homographies are consequently used to guide the matching of local features. Experiments on standard urban localization datasets (Aachen, Extended-CMU-Season, RobotCar-Season) show that OGuL significantly improves localization results with as simple local features as SIFT, and its performance competes with the state-of-the-art CNN-based methods trained for day-to-night localization.
Social Media as an Instant Source of Feedback on Water Quality
Venue: arXiv cs.CL 2022
Year: 2022

This paper focuses on an important environmental challenge; namely, water quality by analyzing the potential of social media as an immediate source of feedback. The main goal of the work is to automatically analyze and retrieve social media posts relevant to water quality with particular attention to posts describing different aspects of water quality, such as watercolor, smell, taste, and related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. In total, three different Neural Networks (NNs) architectures, namely (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and (iii) custom Long short-term memory (LSTM) model, are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Brute Force (BF), Nelder-Mead, and Powell's optimization methods. We also provide an evaluation of the individual models where the highest F1-score of 0.81 is obtained with the BERT model. In merit-based fusion, overall better results are obtained with BF achieving an F1-score score of 0.852. We also provide comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such rigorous analysis of this relatively new topic will provide a baseline for future research.
Obtaining Dyadic Fairness by Optimal Transport
Venue: arXiv cs.LG 2022
Year: 2022

Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning. In this paper, we focus on obtaining fairness for popular link prediction tasks, which are measured by dyadic fairness. A novel pre-processing methodology is proposed to establish dyadic fairness through data repairing based on optimal transport theory. With the well-established theoretical connection between the dyadic fairness for graph link prediction and a conditional distribution alignment problem, the dyadic repairing scheme can be equivalently transformed into a conditional distribution alignment problem. Furthermore, an optimal transport-based dyadic fairness algorithm called DyadicOT is obtained by efficiently solving the alignment problem, satisfying flexibility and unambiguity requirements. The proposed DyadicOT algorithm shows superior results in obtaining fairness compared to other fairness methods on two benchmark graph datasets.
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Venue: arXiv cs.SD 2022
Year: 2022

This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical correlation analysis (CCA-GNN). The proposed approach lays its foundations on a state-of-the-art CCA-GNN that learns representative embeddings by maximizing the correlation between pairs of augmented views of the same input while decorrelating disconnected features. The key idea of the conventional CCA-GNN involves discarding augmentation-variant information and preserving augmentation-invariant information while preventing capturing of redundant information. Our proposed AV CCA-GNN model deals with multimodal representation learning context. Specifically, our model improves contextual AV speech processing by maximizing canonical correlation from augmented views of the same channel and canonical correlation from audio and visual embeddings. In addition, it proposes a positional node encoding that considers a prior-frame sequence distance instead of a feature-space representation when computing the node's nearest neighbors, introducing temporal information in the embeddings through the neighborhood's connectivity. Experiments conducted on the benchmark ChiME3 dataset show that our proposed prior frame-based AV CCA-GNN ensures better feature learning in the temporal context, leading to more energy-efficient speech reconstruction than state-of-the-art CCA-GNN and multilayer perceptron.
Proving Non-Termination and Lower Runtime Bounds with LoAT (System Description)
Venue: arXiv cs.LO 2022
Year: 2022

We present the new version of the Loop Acceleration Tool (LoAT), a powerful tool for proving non-termination and worst-case lower bounds for programs operating on integers. It is based on a novel calculus for loop acceleration, i.e., transforming loops into non-deterministic straight-line code, and for finding non-terminating configurations. To implement it efficiently, LoAT uses a new approach based on SMT solving and unsat cores. An extensive evaluation shows that LoAT is highly competitive with other state-of-the-art tools for proving non-termination. While no other tool is able to deduce worst-case lower bounds for full integer programs, we also demonstrate that LoAT significantly outperforms its predecessors.
The craft and coordination of data curation: complicating "workflow" views of data science
Venue: arXiv cs.DL 2022
Year: 2022

Data curation is the process of making a dataset fit-for-use and archiveable. It is critical to data-intensive science because it makes complex data pipelines possible, makes studies reproducible, and makes data (re)usable. Yet the complexities of the hands-on, technical and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of the data curators invisible; it also makes it harder to tease out the impact curators' work has on the later usability, reliability, and reproducibility of data. To better understand the specific work of data curation -- and thereby, explore ways of showing curators' impact -- we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium of Political and Social Research (ICPSR). We asked, What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulate accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. We find that curators describe a number of craft practices needed to perform their work, which defies the rote sequence of events implied by many lifecycle or workflow models. Further, we show how best practices and craft practices are deeply intertwined.
Optimal Hyperparameters and Structure Setting of Multi-Objective Robust CNN Systems via Generalized Taguchi Method and Objective Vector Norm
Venue: arXiv cs.LG 2022
Year: 2022

Recently, Machine Learning (ML), Artificial Intelligence (AI), and Convolutional Neural Network (CNN) have made huge progress with broad applications, where their systems have deep learning structures and a large number of hyperparameters that determine the quality and performance of the CNNs and AI systems. These systems may have multi-objective ML and AI performance needs. There is a key requirement to find the optimal hyperparameters and structures for multi-objective robust optimal CNN systems. This paper proposes a generalized Taguchi approach to effectively determine the optimal hyperparameters and structure for the multi-objective robust optimal CNN systems via their objective performance vector norm. The proposed approach and methods are applied to a CNN classification system with the original ResNet for CIFAR-10 dataset as a demonstration and validation, which shows the proposed methods are highly effective to achieve an optimal accuracy rate of the original ResNet on CIFAR-10.
Less is More: Supporting Developers in Vulnerability Detection during Code Review
Venue: arXiv cs.SE 2022
Year: 2022

Reviewing source code from a security perspective has proven to be a difficult task. Indeed, previous research has shown that developers often miss even popular and easy-to-detect vulnerabilities during code review. Initial evidence suggests that a significant cause may lie in the reviewers' mental attitude and common practices. In this study, we investigate whether and how explicitly asking developers to focus on security during a code review affects the detection of vulnerabilities. Furthermore, we evaluate the effect of providing a security checklist to guide the security review. To this aim, we conduct an online experiment with 150 participants, of which 71% report to have three or more years of professional development experience. Our results show that simply asking reviewers to focus on security during the code review increases eight times the probability of vulnerability detection. The presence of a security checklist does not significantly improve the outcome further, even when the checklist is tailored to the change under review and the existing vulnerabilities in the change. These results provide evidence supporting the mental attitude hypothesis and call for further work on security checklists' effectiveness and design. Data and materials: https://doi.org/10.5281/zenodo.6026291
Outside Looking In: Approaches to Content Moderation in End-to-End Encrypted Systems
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we assess existing technical proposals for content moderation in End-to-End Encryption (E2EE) services. First, we explain the various tools in the content moderation toolbox, how they are used, and the different phases of the moderation cycle, including detection of unwanted content. We then lay out a definition of encryption and E2EE, which includes privacy and security guarantees for end-users, before assessing current technical proposals for the detection of unwanted content in E2EE services against those guarantees. We find that technical approaches for user-reporting and meta-data analysis are the most likely to preserve privacy and security guarantees for end-users. Both provide effective tools that can detect significant amounts of different types of problematic content on E2EE services, including abusive and harassing messages, spam, mis- and disinformation, and CSAM, although more research is required to improve these tools and better measure their effectiveness. Conversely, we find that other techniques that purport to facilitate content detection in E2EE systems have the effect of undermining key security guarantees of E2EE systems.
Analyzing Medical Data with Process Mining: a COVID-19 Case Study
Venue: arXiv cs.DB 2022
Year: 2022

The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients' medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the representation of medical information as sequences of events, effectively allowing to perform process mining analyses. In this paper, we will present some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19. We show that process mining techniques are able to reconstruct a model of the ICU treatments for COVID patients.
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Venue: arXiv cs.LG 2022
Year: 2022

Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e.g., Bellman-completeness) and the data coverage (e.g., all-policy concentrability). Despite the recent efforts on relaxing these assumptions, existing works are only able to relax one of the two factors, leaving the strong assumption on the other factor intact. As an important open problem, can we achieve sample-efficient offline RL with weak assumptions on both factors? In this paper we answer the question in the positive. We analyze a simple algorithm based on the primal-dual formulation of MDPs, where the dual variables (discounted occupancy) are modeled using a density-ratio function against offline data. With proper regularization, we show that the algorithm enjoys polynomial sample complexity, under only realizability and single-policy concentrability. We also provide alternative analyses based on different assumptions to shed light on the nature of primal-dual algorithms for offline RL.
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such that the new point maximizes prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. We use the Feynman AI benchmark set of equations to examine the ability of our method to find appropriate models using fewer data points. The approach was found to successfully rediscover 72 of the 100 Feynman equations using as few data points as possible, and without use of domain expertise or data translation.